Statistical relational learning of trust

Abstract

The learning of trust and distrust is a crucial aspect of social interaction among autonomous, mentally-opaque agents. In this work, we address the learning of trust based on past observations and context information. We argue that from the truster’s point of view trust is best expressed as one of several relations that exist between the agent to be trusted (trustee) and the state of the environment. Besides attributes expressing trustworthiness, additional relations might describe commitments made by the trustee with regard to the current situation, like: a seller offers a certain price for a specific product. We show how to implement and learn context-sensitive trust using statistical relational learning in form of a Dirichlet process mixture model called Infinite Hidden Relational Trust Model (IHRTM). The practicability and effectiveness of our approach is evaluated empirically on user-ratings gathered from eBay. Our results suggest that (i) the inherent clustering achieved in the algorithm allows the truster to characterize the structure of a trust-situation and provides meaningful trust assessments; (ii) utilizing the collaborative filtering effect associated with relational data does improve trust assessment performance; (iii) by learning faster and transferring knowledge more effectively we improve cold start performance and can cope better with dynamic behavior in open multiagent systems. The later is demonstrated with interactions recorded from a strategic two-player negotiation scenario.

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Correspondence to Achim Rettinger.

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Editors: S.V.N. Vishwanathan, Samuel Kaski, Jennifer Neville, and Stefan Wrobel.

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Rettinger, A., Nickles, M. & Tresp, V. Statistical relational learning of trust. Mach Learn 82, 191–209 (2011). https://doi.org/10.1007/s10994-010-5211-x

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Keywords

  • Relational learning
  • Computational trust
  • Social computing
  • Infinite hidden relational models
  • Initial trust